1. Field of the Invention
The present invention relates to an apparatus and a method for segmenting an image into a plurality of areas.
2. Description of the Related Art
In the related arts, a study to segment an image into a plurality of meaningful areas has been performed. For example, such a study has been performed with respect to a Segmentation task disclosed in M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The PASCAL Visual Object Classes (VOC) challenge”, International Journal of Computer Vision. Vol. 88(2), 2010 (hereinbelow, referred to as Literature 1). In such a process for segmenting an image, first, a method whereby an image is segmented into small areas constructed by a plurality of similar adjacent pixels called superpixels, a feature quantity is extracted from each of the divided areas, and the areas are integrated and classified on the basis of the extracted feature quantities has been proposed. For example, a method whereby each area is classified into a class such as sky, tree, road, or the like by a neural network which was previously learned has been disclosed in Richard Socher, Cliff Lin, Andrew Y. Ng, and Christopher D. Manning, “Parsing Natural Scenes and Natural Language with Recursive Neural Networks”, ICML 2011 (hereinbelow, referred to as Literature 2). As a method of generating superpixels which are used as a preprocess, a clustering or a graph expression is used (for example, refer to Felzenszwalb, P., Huttenlocher, D., “Efficient graph-based image segmentation”, International Journal of Computer Vision. 2004 (hereinbelow, referred to as Literature 3) and Radhakrishna Achanta, Appu Shaji, Kevin smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk, “SLIC Superpixels”, EPFL Technical Report 149300, June 2010 (hereinbelow, referred to as Literature 4).
However, for example, in the case where an image is segmented into superpixels by the method disclosed in Literature 3 and the classification disclosed in Literature 2 is performed to the divided superpixels, the following problems occur.
First, according to the method disclosed in Literature 3, there is a case where the number of areas of the superpixels becomes very large as a result of the area segmentation. For example, when textures such as tree, grass, and the like exist in the whole image, an edge portion is liable to be segmented and a large quantity of areas are generated. On the other hand, since the process which is executed for the classification is very complicated, if the number of superpixels is large, it takes a long time for the process.
Also with respect to an area size, in the area segmentation according to the method disclosed in Literature 3, an edge is sensitively separated in an area having the textures. Therefore, as a result, not only the number of areas increases but also many small areas are generated. Further, in the classification disclosed in Literature 2, there is such a problem that, to an area of a small size, a discrimination precision deteriorates. This is because, in the classification, although a category is discriminated by various feature quantities which are extracted from the areas, the feature quantities which are extracted from the small area are not stable. For example, if there are noises in a pixel value, although an area of a large size is difficult to be subjected to such an influence by the noises, the small area is liable to be influenced and there is a case where a feature different from a feature which the user inherently wants to extract is extracted.
It is an aspect of the invention to simply classify an image into a plurality of meaningful areas at a high precision.